Overview

Brought to you by YData

Dataset statistics

Number of variables15
Number of observations1338
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory156.9 KiB
Average record size in memory120.1 B

Variable types

Numeric9
Categorical6

Alerts

Anual_Salary is highly overall correlated with Hospital_expenditure and 5 other fieldsHigh correlation
Hospital_expenditure is highly overall correlated with Anual_Salary and 4 other fieldsHigh correlation
NUmber_of_past_hospitalizations is highly overall correlated with Anual_Salary and 4 other fieldsHigh correlation
age is highly overall correlated with charges and 1 other fieldsHigh correlation
charges is highly overall correlated with Anual_Salary and 6 other fieldsHigh correlation
num_of_steps is highly overall correlated with Anual_Salary and 6 other fieldsHigh correlation
past_consultations is highly overall correlated with Anual_Salary and 3 other fieldsHigh correlation
smoker_yes is highly overall correlated with Anual_Salary and 5 other fieldsHigh correlation
children has 574 (42.9%) zerosZeros

Reproduction

Analysis started2025-10-27 17:51:55.396723
Analysis finished2025-10-27 17:52:07.080559
Duration11.68 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

age
Real number (ℝ)

High correlation 

Distinct47
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.307922
Minimum18
Maximum64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2025-10-27T17:52:07.196440image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile18
Q127
median39
Q351
95-th percentile62
Maximum64
Range46
Interquartile range (IQR)24

Descriptive statistics

Standard deviation13.987523
Coefficient of variation (CV)0.35584489
Kurtosis-1.2299762
Mean39.307922
Median Absolute Deviation (MAD)12
Skewness0.046115549
Sum52594
Variance195.65081
MonotonicityNot monotonic
2025-10-27T17:52:07.349488image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
1869
 
5.2%
1966
 
4.9%
3934
 
2.5%
4729
 
2.2%
4829
 
2.2%
4529
 
2.2%
4629
 
2.2%
5229
 
2.2%
5029
 
2.2%
5129
 
2.2%
Other values (37)966
72.2%
ValueCountFrequency (%)
1869
5.2%
1966
4.9%
2028
2.1%
2128
2.1%
2226
 
1.9%
2327
 
2.0%
2428
2.1%
2527
 
2.0%
2628
2.1%
2728
2.1%
ValueCountFrequency (%)
6422
1.6%
6323
1.7%
6223
1.7%
6123
1.7%
6023
1.7%
5925
1.9%
5825
1.9%
5726
1.9%
5626
1.9%
5526
1.9%

bmi
Real number (ℝ)

Distinct547
Distinct (%)40.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.664518
Minimum15.96
Maximum53.13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2025-10-27T17:52:07.478827image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum15.96
5-th percentile21.256
Q126.315
median30.4
Q334.65625
95-th percentile41.106
Maximum53.13
Range37.17
Interquartile range (IQR)8.34125

Descriptive statistics

Standard deviation6.0948532
Coefficient of variation (CV)0.19875914
Kurtosis-0.045394383
Mean30.664518
Median Absolute Deviation (MAD)4.18
Skewness0.28449493
Sum41029.125
Variance37.147236
MonotonicityNot monotonic
2025-10-27T17:52:07.615588image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32.313
 
1.0%
28.319
 
0.7%
31.358
 
0.6%
30.8758
 
0.6%
30.88
 
0.6%
34.18
 
0.6%
30.4958
 
0.6%
28.888
 
0.6%
24.327
 
0.5%
32.7757
 
0.5%
Other values (537)1254
93.7%
ValueCountFrequency (%)
15.961
 
0.1%
16.8152
0.1%
17.1951
 
0.1%
17.293
0.2%
17.3851
 
0.1%
17.41
 
0.1%
17.481
 
0.1%
17.671
 
0.1%
17.7651
 
0.1%
17.81
 
0.1%
ValueCountFrequency (%)
53.131
0.1%
52.581
0.1%
50.381
0.1%
49.061
0.1%
48.071
0.1%
47.741
0.1%
47.61
0.1%
47.521
0.1%
47.411
0.1%
46.751
0.1%

children
Real number (ℝ)

Zeros 

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0904335
Minimum0
Maximum5
Zeros574
Zeros (%)42.9%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2025-10-27T17:52:07.726027image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile3
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.199619
Coefficient of variation (CV)1.1001304
Kurtosis0.19055935
Mean1.0904335
Median Absolute Deviation (MAD)1
Skewness0.93462317
Sum1459
Variance1.4390857
MonotonicityNot monotonic
2025-10-27T17:52:07.809638image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0574
42.9%
1326
24.4%
2240
17.9%
3156
 
11.7%
425
 
1.9%
517
 
1.3%
ValueCountFrequency (%)
0574
42.9%
1326
24.4%
2240
17.9%
3156
 
11.7%
425
 
1.9%
517
 
1.3%
ValueCountFrequency (%)
517
 
1.3%
425
 
1.9%
3156
 
11.7%
2240
17.9%
1326
24.4%
0574
42.9%

Claim_Amount
Real number (ℝ)

Distinct1325
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33364.874
Minimum1920.1363
Maximum77277.988
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2025-10-27T17:52:07.922692image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1920.1363
5-th percentile8327.4096
Q120947.645
median33700.311
Q344978.873
95-th percentile57500.208
Maximum77277.988
Range75357.852
Interquartile range (IQR)24031.228

Descriptive statistics

Standard deviation15535.346
Coefficient of variation (CV)0.46561979
Kurtosis-0.69136873
Mean33364.874
Median Absolute Deviation (MAD)11950.315
Skewness0.098037984
Sum44642202
Variance2.4134696 × 108
MonotonicityNot monotonic
2025-10-27T17:52:08.073640image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33700.3106814
 
1.0%
34007.667391
 
0.1%
34827.574071
 
0.1%
27682.72771
 
0.1%
12770.132491
 
0.1%
15073.03781
 
0.1%
56840.238641
 
0.1%
28359.894231
 
0.1%
37441.402361
 
0.1%
8819.7971071
 
0.1%
Other values (1315)1315
98.3%
ValueCountFrequency (%)
1920.1362681
0.1%
2912.5905841
0.1%
3037.7259191
0.1%
3370.3983231
0.1%
3768.6030331
0.1%
3830.100391
0.1%
3927.8920671
0.1%
3965.6064641
0.1%
4149.9504861
0.1%
4203.1097131
0.1%
ValueCountFrequency (%)
77277.988481
0.1%
76028.853481
0.1%
73894.367041
0.1%
72760.929791
0.1%
72147.094041
0.1%
71885.174471
0.1%
71776.980231
0.1%
71219.04831
0.1%
70963.847231
0.1%
69927.516641
0.1%

past_consultations
Real number (ℝ)

High correlation 

Distinct39
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.215247
Minimum1
Maximum40
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2025-10-27T17:52:08.206286image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q19
median15
Q320
95-th percentile29
Maximum40
Range39
Interquartile range (IQR)11

Descriptive statistics

Standard deviation7.450962
Coefficient of variation (CV)0.48970367
Kurtosis-0.17971368
Mean15.215247
Median Absolute Deviation (MAD)5
Skewness0.41536423
Sum20358
Variance55.516835
MonotonicityNot monotonic
2025-10-27T17:52:08.357538image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
2070
 
5.2%
1570
 
5.2%
1068
 
5.1%
2168
 
5.1%
1868
 
5.1%
1366
 
4.9%
963
 
4.7%
1661
 
4.6%
1960
 
4.5%
1758
 
4.3%
Other values (29)686
51.3%
ValueCountFrequency (%)
12
 
0.1%
223
 
1.7%
324
 
1.8%
435
2.6%
545
3.4%
638
2.8%
754
4.0%
856
4.2%
963
4.7%
1068
5.1%
ValueCountFrequency (%)
401
 
0.1%
383
 
0.2%
371
 
0.1%
364
 
0.3%
357
0.5%
344
 
0.3%
334
 
0.3%
3213
1.0%
3113
1.0%
3013
1.0%

num_of_steps
Real number (ℝ)

High correlation 

Distinct1335
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean910014.33
Minimum695430
Maximum1107872
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2025-10-27T17:52:08.493078image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum695430
5-th percentile746831.1
Q1847489.75
median914300
Q3971510
95-th percentile1062834.1
Maximum1107872
Range412442
Interquartile range (IQR)124020.25

Descriptive statistics

Standard deviation91783.198
Coefficient of variation (CV)0.10085907
Kurtosis-0.61835488
Mean910014.33
Median Absolute Deviation (MAD)63276.5
Skewness-0.082096436
Sum1.2175992 × 109
Variance8.4241555 × 109
MonotonicityNot monotonic
2025-10-27T17:52:08.635016image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9143004
 
0.3%
9492381
 
0.1%
9543631
 
0.1%
9464751
 
0.1%
9599851
 
0.1%
9465981
 
0.1%
9436491
 
0.1%
9482961
 
0.1%
9439001
 
0.1%
9521031
 
0.1%
Other values (1325)1325
99.0%
ValueCountFrequency (%)
6954301
0.1%
6991571
0.1%
6991591
0.1%
7002501
0.1%
7012271
0.1%
7023411
0.1%
7044251
0.1%
7064231
0.1%
7067961
0.1%
7115461
0.1%
ValueCountFrequency (%)
11078721
0.1%
11068211
0.1%
11003281
0.1%
10959601
0.1%
10920051
0.1%
10912791
0.1%
10912671
0.1%
10866351
0.1%
10865941
0.1%
10854961
0.1%

Hospital_expenditure
Real number (ℝ)

High correlation 

Distinct1335
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15816825
Minimum29452.533
Maximum2.616317 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2025-10-27T17:52:08.770620image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum29452.533
5-th percentile1258906.9
Q14084941
median7490336.9
Q310826298
95-th percentile77451060
Maximum2.616317 × 108
Range2.6160225 × 108
Interquartile range (IQR)6741357.2

Descriptive statistics

Standard deviation26656991
Coefficient of variation (CV)1.6853566
Kurtosis18.935944
Mean15816825
Median Absolute Deviation (MAD)3398346
Skewness3.7534547
Sum2.1162912 × 1010
Variance7.1059515 × 1014
MonotonicityNot monotonic
2025-10-27T17:52:08.912362image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7490336.9054
 
0.3%
12144973.481
 
0.1%
5233108.4841
 
0.1%
4895703.0171
 
0.1%
2921439.0061
 
0.1%
8228465.3051
 
0.1%
2289506.1121
 
0.1%
7290091.6661
 
0.1%
6113973.7711
 
0.1%
8658220.4381
 
0.1%
Other values (1325)1325
99.0%
ValueCountFrequency (%)
29452.532961
0.1%
35822.437571
0.1%
57647.088341
0.1%
77956.027631
0.1%
87483.37321
0.1%
87572.092631
0.1%
104079.83711
0.1%
174710.73561
0.1%
187778.43321
0.1%
249159.42531
0.1%
ValueCountFrequency (%)
261631699.31
0.1%
252892382.61
0.1%
223644981.31
0.1%
201515184.81
0.1%
170380500.51
0.1%
148034634.61
0.1%
144061589.91
0.1%
126353660.61
0.1%
1236279271
0.1%
122405879.81
0.1%

NUmber_of_past_hospitalizations
Categorical

High correlation 

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size68.1 KiB
1.0
959 
2.0
227 
0.0
150 
3.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4014
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.0959
71.7%
2.0227
 
17.0%
0.0150
 
11.2%
3.02
 
0.1%

Length

2025-10-27T17:52:09.033747image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-27T17:52:09.117302image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0959
71.7%
2.0227
 
17.0%
0.0150
 
11.2%
3.02
 
0.1%

Most occurring characters

ValueCountFrequency (%)
01488
37.1%
.1338
33.3%
1959
23.9%
2227
 
5.7%
32
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)4014
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
01488
37.1%
.1338
33.3%
1959
23.9%
2227
 
5.7%
32
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4014
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
01488
37.1%
.1338
33.3%
1959
23.9%
2227
 
5.7%
32
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4014
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
01488
37.1%
.1338
33.3%
1959
23.9%
2227
 
5.7%
32
 
< 0.1%

Anual_Salary
Real number (ℝ)

High correlation 

Distinct1333
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6866356 × 108
Minimum2747071.9
Maximum4.1171966 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2025-10-27T17:52:09.232443image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2747071.9
5-th percentile28048141
Q177550855
median1.4193609 × 108
Q33.2252025 × 108
95-th percentile1.7417077 × 109
Maximum4.1171966 × 109
Range4.1144496 × 109
Interquartile range (IQR)2.4496939 × 108

Descriptive statistics

Standard deviation5.6581568 × 108
Coefficient of variation (CV)1.5347752
Kurtosis7.4130177
Mean3.6866356 × 108
Median Absolute Deviation (MAD)77679633
Skewness2.6227419
Sum4.9327185 × 1011
Variance3.2014739 × 1017
MonotonicityNot monotonic
2025-10-27T17:52:09.379460image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
141936093.26
 
0.4%
207558006.71
 
0.1%
174967925.21
 
0.1%
204185331.21
 
0.1%
174312311.31
 
0.1%
203539312.11
 
0.1%
2360174761
 
0.1%
172852746.81
 
0.1%
169599117.71
 
0.1%
208172884.91
 
0.1%
Other values (1323)1323
98.9%
ValueCountFrequency (%)
2747071.9081
0.1%
3150786.261
0.1%
3550883.6011
0.1%
4038871.6641
0.1%
4979935.531
0.1%
6062705.3471
0.1%
6735551.4721
0.1%
7086383.7291
0.1%
7109737.4721
0.1%
8140885.21
0.1%
ValueCountFrequency (%)
41171966371
0.1%
40063585051
0.1%
36408066831
0.1%
34842161171
0.1%
31011073701
0.1%
27806421851
0.1%
26827045091
0.1%
24895079771
0.1%
24632218521
0.1%
24463484181
0.1%

charges
Real number (ℝ)

High correlation 

Distinct1337
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13270.422
Minimum1121.8739
Maximum63770.428
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2025-10-27T17:52:09.806269image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1121.8739
5-th percentile1757.7534
Q14740.2872
median9382.033
Q316639.913
95-th percentile41181.828
Maximum63770.428
Range62648.554
Interquartile range (IQR)11899.625

Descriptive statistics

Standard deviation12110.011
Coefficient of variation (CV)0.91255659
Kurtosis1.6062987
Mean13270.422
Median Absolute Deviation (MAD)5018.7571
Skewness1.5158797
Sum17755825
Variance1.4665237 × 108
MonotonicityIncreasing
2025-10-27T17:52:09.947444image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1639.56312
 
0.1%
12949.15541
 
0.1%
12928.79111
 
0.1%
12925.8861
 
0.1%
12913.99241
 
0.1%
12890.057651
 
0.1%
12829.45511
 
0.1%
12815.444951
 
0.1%
12797.209621
 
0.1%
12741.167451
 
0.1%
Other values (1327)1327
99.2%
ValueCountFrequency (%)
1121.87391
0.1%
1131.50661
0.1%
1135.94071
0.1%
1136.39941
0.1%
1137.0111
0.1%
1137.46971
0.1%
1141.44511
0.1%
1146.79661
0.1%
1149.39591
0.1%
1163.46271
0.1%
ValueCountFrequency (%)
63770.428011
0.1%
62592.873091
0.1%
60021.398971
0.1%
58571.074481
0.1%
55135.402091
0.1%
52590.829391
0.1%
51194.559141
0.1%
49577.66241
0.1%
48970.24761
0.1%
48885.135611
0.1%

sex_male
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size68.1 KiB
1.0
676 
0.0
662 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4014
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0676
50.5%
0.0662
49.5%

Length

2025-10-27T17:52:10.071694image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-27T17:52:10.137498image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0676
50.5%
0.0662
49.5%

Most occurring characters

ValueCountFrequency (%)
02000
49.8%
.1338
33.3%
1676
 
16.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)4014
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
02000
49.8%
.1338
33.3%
1676
 
16.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4014
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
02000
49.8%
.1338
33.3%
1676
 
16.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4014
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
02000
49.8%
.1338
33.3%
1676
 
16.8%

smoker_yes
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size68.1 KiB
0.0
1064 
1.0
274 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4014
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.01064
79.5%
1.0274
 
20.5%

Length

2025-10-27T17:52:10.221874image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-27T17:52:10.285954image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.01064
79.5%
1.0274
 
20.5%

Most occurring characters

ValueCountFrequency (%)
02402
59.8%
.1338
33.3%
1274
 
6.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)4014
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
02402
59.8%
.1338
33.3%
1274
 
6.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4014
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
02402
59.8%
.1338
33.3%
1274
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4014
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
02402
59.8%
.1338
33.3%
1274
 
6.8%

region_northwest
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size68.1 KiB
0.0
1013 
1.0
325 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4014
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.01013
75.7%
1.0325
 
24.3%

Length

2025-10-27T17:52:10.385218image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-27T17:52:10.453784image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.01013
75.7%
1.0325
 
24.3%

Most occurring characters

ValueCountFrequency (%)
02351
58.6%
.1338
33.3%
1325
 
8.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)4014
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
02351
58.6%
.1338
33.3%
1325
 
8.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4014
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
02351
58.6%
.1338
33.3%
1325
 
8.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4014
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
02351
58.6%
.1338
33.3%
1325
 
8.1%

region_southeast
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size68.1 KiB
0.0
974 
1.0
364 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4014
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0974
72.8%
1.0364
 
27.2%

Length

2025-10-27T17:52:10.539690image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-27T17:52:10.603226image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0974
72.8%
1.0364
 
27.2%

Most occurring characters

ValueCountFrequency (%)
02312
57.6%
.1338
33.3%
1364
 
9.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)4014
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
02312
57.6%
.1338
33.3%
1364
 
9.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4014
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
02312
57.6%
.1338
33.3%
1364
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4014
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
02312
57.6%
.1338
33.3%
1364
 
9.1%

region_southwest
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size68.1 KiB
0.0
1013 
1.0
325 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4014
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.01013
75.7%
1.0325
 
24.3%

Length

2025-10-27T17:52:10.689110image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-27T17:52:10.761148image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.01013
75.7%
1.0325
 
24.3%

Most occurring characters

ValueCountFrequency (%)
02351
58.6%
.1338
33.3%
1325
 
8.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)4014
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
02351
58.6%
.1338
33.3%
1325
 
8.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4014
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
02351
58.6%
.1338
33.3%
1325
 
8.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4014
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
02351
58.6%
.1338
33.3%
1325
 
8.1%

Interactions

2025-10-27T17:52:05.663259image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-27T17:51:56.240251image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-27T17:51:57.394128image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-27T17:51:58.369213image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-27T17:51:59.284579image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-27T17:52:00.828904image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-27T17:52:02.328969image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-27T17:52:03.409077image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-27T17:52:04.445709image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-27T17:52:05.773401image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-27T17:51:56.595282image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-27T17:51:57.497382image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-27T17:51:58.461972image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-27T17:51:59.395201image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-27T17:52:00.987731image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-27T17:52:02.481400image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-27T17:52:03.514719image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-27T17:52:04.546543image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-27T17:52:05.908499image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-27T17:51:56.690756image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-27T17:51:57.603132image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-27T17:51:58.561592image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-27T17:51:59.511164image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-27T17:52:01.141220image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-27T17:52:02.638664image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-27T17:52:03.631764image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-27T17:52:04.657061image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-27T17:52:06.021308image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-27T17:51:56.800571image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-27T17:51:57.705617image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-27T17:51:58.654398image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-27T17:51:59.813931image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-27T17:52:01.295716image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-27T17:52:02.756569image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-27T17:52:03.741987image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-27T17:52:04.997069image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-27T17:52:06.143942image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-27T17:51:56.906278image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-27T17:51:57.832327image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-27T17:51:58.771312image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-27T17:52:00.000062image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-27T17:52:01.459663image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-27T17:52:02.860096image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-27T17:52:03.866516image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-27T17:52:05.119742image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-27T17:52:06.283470image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-27T17:51:57.005553image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-27T17:51:57.947314image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-27T17:51:58.891509image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-27T17:52:00.182205image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-27T17:52:01.624463image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-27T17:52:02.970502image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-27T17:52:03.987652image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-27T17:52:05.244445image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-27T17:52:06.391504image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-27T17:51:57.105651image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-27T17:51:58.045753image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-27T17:51:58.992095image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-27T17:52:00.343783image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-27T17:52:01.790004image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-27T17:52:03.100119image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-27T17:52:04.095364image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-27T17:52:05.347056image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-27T17:52:06.507603image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-27T17:51:57.202876image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-27T17:51:58.151635image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-27T17:51:59.091245image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-27T17:52:00.505348image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-27T17:52:01.966084image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-27T17:52:03.198177image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-27T17:52:04.220223image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-27T17:52:05.452342image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-27T17:52:06.620594image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-27T17:51:57.297440image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-27T17:51:58.256407image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-27T17:51:59.188566image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-27T17:52:00.663156image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-27T17:52:02.131649image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-27T17:52:03.301480image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-27T17:52:04.329859image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-27T17:52:05.558684image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-10-27T17:52:10.839246image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Anual_SalaryClaim_AmountHospital_expenditureNUmber_of_past_hospitalizationsagebmichargeschildrennum_of_stepspast_consultationsregion_northwestregion_southeastregion_southwestsex_malesmoker_yes
Anual_Salary1.0000.3390.6640.7800.4540.1300.9410.0880.9350.5090.1080.1070.1130.0690.755
Claim_Amount0.3391.0000.2790.3120.1190.0830.3660.0460.3620.2320.0290.0520.0470.0460.390
Hospital_expenditure0.6640.2791.0000.7500.1800.1230.6740.0210.6720.4180.0570.1190.0480.0710.670
NUmber_of_past_hospitalizations0.7800.3120.7501.0000.3740.1570.7130.1860.7520.3700.0420.1200.0000.0910.677
age0.4540.1190.1800.3741.0000.1110.5260.0530.5190.1590.0000.0000.0000.0000.049
bmi0.1300.0830.1230.1570.1111.0000.1200.0110.1200.1160.1390.2710.0050.0000.000
charges0.9410.3660.6740.7130.5260.1201.0000.1350.9930.5190.0000.0970.0860.0630.832
children0.0880.0460.0210.1860.0530.0110.1351.0000.1340.0590.0450.0000.0000.0000.037
num_of_steps0.9350.3620.6720.7520.5190.1200.9930.1341.0000.5170.0450.1230.0000.1570.803
past_consultations0.5090.2320.4180.3700.1590.1160.5190.0590.5171.0000.0000.0570.0000.0560.548
region_northwest0.1080.0290.0570.0420.0000.1390.0000.0450.0450.0001.0000.3430.3180.0000.022
region_southeast0.1070.0520.1190.1200.0000.2710.0970.0000.1230.0570.3431.0000.3430.0000.061
region_southwest0.1130.0470.0480.0000.0000.0050.0860.0000.0000.0000.3180.3431.0000.0000.022
sex_male0.0690.0460.0710.0910.0000.0000.0630.0000.1570.0560.0000.0000.0001.0000.069
smoker_yes0.7550.3900.6700.6770.0490.0000.8320.0370.8030.5480.0220.0610.0220.0691.000

Missing values

2025-10-27T17:52:06.802585image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-10-27T17:52:06.958283image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

agebmichildrenClaim_Amountpast_consultationsnum_of_stepsHospital_expenditureNUmber_of_past_hospitalizationsAnual_Salarychargessex_malesmoker_yesregion_northwestregion_southeastregion_southwest
018.023.210.029087.54313017.0715428.04.720921e+060.05.578497e+071121.87391.00.00.01.00.0
118.030.140.039053.6743707.0699157.04.329832e+060.01.370089e+071131.50661.00.00.01.00.0
218.033.330.039023.62759019.0702341.06.884861e+060.07.352311e+071135.94071.00.00.01.00.0
318.033.660.028185.39332011.0700250.04.274774e+060.07.581968e+071136.39941.00.00.01.00.0
418.034.100.014697.85941016.0711584.03.787294e+060.02.301232e+071137.01101.00.00.01.00.0
518.034.430.026488.33912020.0717162.03.696161e+060.01.419361e+081137.46971.00.00.01.00.0
618.037.290.033217.36548013.0699159.08.765292e+050.06.906067e+071141.44511.00.00.01.00.0
718.041.140.046770.58533012.0706423.04.486741e+060.09.719378e+071146.79661.00.00.01.00.0
818.043.010.09715.65041117.0914300.09.216440e+060.05.888197e+071149.39591.00.00.01.00.0
918.053.130.017046.58515019.0704425.01.458972e+060.09.426182e+071163.46271.00.00.01.00.0
agebmichildrenClaim_Amountpast_consultationsnum_of_stepsHospital_expenditureNUmber_of_past_hospitalizationsAnual_Salarychargessex_malesmoker_yesregion_northwestregion_southeastregion_southwest
132844.038.0600.076028.8534825.01072324.0122405879.82.02.430290e+0948885.135610.01.00.01.00.0
132959.041.1401.053104.9262138.01079931.0126353660.62.02.399896e+0948970.247601.01.00.01.00.0
133064.036.9602.065641.2482328.01091279.0123627927.02.02.489508e+0949577.662401.01.00.01.00.0
133128.036.4001.055590.7552726.01080113.0144061589.92.02.682705e+0951194.559141.01.00.00.01.0
133260.032.8000.077277.9884840.01095960.0148034634.62.02.780642e+0952590.829391.01.00.00.01.0
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